Investigation of the Effectiveness of Usage of Graph Databases for Big Data Analysis

The purpose of research. The purpose of this work is to study graph models of databases and develop a methodology for comparative analysis of database models. The theoretical and methodological basis of the study was the fundamental scientific works of domestic and foreign authors in the field of basic problems of database theory, algorithm theory, graph theory, data processing structures and methods.

Methods. The paper uses methods of structural, comparative and content analysis, as well as statistical methods of information processing and methods of graph theory. As a result of the conducted research, the authors justified the features, advantages and disadvantages of using a graph data model.

Results. The relevance of this study is due to the intensive development of information technologies intended for the economic development of the country, the pandemic and the geopolitical situation in the world. These prerequisites orient researchers to use new methods of data processing and analysis. However, it is possible to optimize big data processing processes not only with the help of powerful new algorithms, but also with the use of fundamentally different data structures and models other than relational.

The paper presents applied examples of using the graph model of databases in various subject areas. A method of comparative analysis of data models in relation to big data analysis has been developed. The main points of data model design are highlighted: system scaling, compliance with requirements and standards, the ability to change data model structures, language complexity, performance and data processing speed. The proposed technique made it possible to numerically evaluate the effectiveness of graph models.
Conclusion. The theoretical significance of the research consists in the development of methodological and technological approaches to the analysis of big data and the formation of structures and databases. The practical results of the study can be useful to large IT companies, as well as to the financial, logistics and commercial sectors, where the problem of big data analysis and research is most acute. 

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